A Variable Speed Compressor Motor Drive. Abdul Halim Mohd Yatim and Wahyu Mulyo Utomo. Energy Conversion Department -Electrical Engineering Faculty.
Neural Network Efficiency Optimization Control of A Variable Speed Compressor Motor Drive Abdul Halim Mohd Yatim and Wahyu Mulyo Utomo
Energy Conversion Department -Electrical Engineering Faculty Universiti Teknologi Malaysia Johor Baru 813 10 Malaysia
Abstract- This paper presents a new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction
into two categories [7,8,9,10]: loss-model-based controller (LMC) and search controller (SC) method.
Keywords- Induction motor, efficiency optimization control, neural network control
Basically, the LMC method uses analytical computation of the motor losses to optimize the efficiency. The optimum flux is determined by deriving the equation of the motor power losses against to the flux. The previous work on LMC method shows that the main advantage is simplificity of this method i.e. does not require extra hardware. However, it is mandatory that an accurate knowledge of motor parameters is known, which change considerably with temperature, saturation and skin effect. In real-time application, the difficulty in measuring the motor parameters of the loss model does not permit the implementation of the LMC [7]. In addition some of the motor losses such as stray losses and iron losses are also very complex to be determined.
motors. The efficiency of induction motor at low load operation and at rated flux is very low compared when operating at full load due to excessively increase in iron losses. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. In this paper, the proposed controller is designed to generate both the voltage and frequency reference signals simultaneously. The proposed controller was simulated for variable speed compressor application. The results obtained clearly show that the efficiency at low speed is significantly increased. Besides that the speed of the motor can be maintained. The simulation results are also verified by experiment.
I. INTRODUCTION World wide, approximately around 7000O of total electrical energy is consumed by electric motor [1]. Around 96% of the total electric motors are consumed by the induction motor [2]. In terms of the efficiency, operation of the induction motor at rated flux results in good utilization of the motor iron hence high efficiency and torque per stator ampere can be achieved At rated flux the nominal electromagnetic torque can be developed at all frequencies. However, at light load the motor flux may be greater than necessary for development of required load torque. In this condition the iron and stator copper losses increase excessively hence the total losses become high and the efficiency drops dramatically [2,3,4]. According to the load condition, the induction motor drive efficiency can be increased by reducing the motor air gap flux. In scalar control method, the flux can be indirectly controlled by adjusting both stator voltage and frequency [5]. The main problem of the efficiency optimization control of the induction motor drive system at variable load operation is to obtain the optimum motor flux level that minimizes the total motor losses and the maximum efficiency is achieved [6,7]. At the same time it is also important to ascertain that the rotor speed of the motor is still stable. A number of methods have been published on efficiency optimization control of the induction motor drive system. The technique allowing the efficiency improvement can be divided
1 -4244-0743-5/07/$20.OO ©2007 IEEE
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Search controller (SC) method is an induction motor efficiency control technique based on the minimum input power tracking approach. The principle of this method is that the input power is measured and then the motor flux function is gradually decreased to achieve the minimum input power associated to the minimum power losses or maximum efficiency. The previous works on the SC method show that to achieve optimal efficiency, the flux is decremented in steps until the measured input power for a certain load torque and speed condition settles down to the lowest value. This method does not require any knowledge of the motor parameters, is completely insensitive to motor parameter variation and the algorithm is applicable universally to any arbitrary drive [4].Some implementations of the intelligent control method such as fuzzy logic and neural network control for this method have many advantages over classical search control methods proposed in literatures [1 1, 12,13,14,15,16]. In this paper, a new neural network search controller with real-time learning algorithm based on scalar control model is proposed. The proposed controller is explained in Section II. The simulation and experimental results, related to a 0.25hp induction motor drive and dynamometer as a compressor load prototype are given in section III. The results of the increasing efficiency are compared to those obtained with the constant Volt per Hertz approach. Finally, section IV summarizes the conclusions.
11.
EFFICIENCY OPTIMIZATION CONTROL DESIGN
poad
A. Development ofNeural Network Efficiency Optimization Controller Based on the input power measurement, a direct neural network control reference model of efficiency optimization is developed. The block diagram of the proposed neural network controller for efficiency optimization of a variable speed compressor drive is presented in Fig. 1. The controller is implemented through the use of a digital signal processor as indicated by dashed outline in Fig. 1.
---
__ Z~~~~~~~~~~~~~~~L
------------------
+~~
(kL N )N
(3)
where: Pload is compressor load power
If efficiency of the motor drive is targeted with the efficiency at nominal speed (1qnom) for all speed operation, the input power reference model can be defined as:
ref -
~~~~P Lk(N ef
~
Pload
(4)
!!nom )7 nom
-
Iwhere: P*ef is the input power motor reference and 'nom is the CONTROL i ' i X X ; nominal motor efficient.
Input pDwer ref nrudl
--_ _ __________
} I'mB.FfStructure ofModel reference Neural Network Controller z L ! to design the neural network controller, the Basically, l tP T P, ; number of inputs and outputs neuron at each layer are equal to the number of input and output signals of the system e X respectively. Further the number of hidden layers and the total neurons is depended on the complexity of the system and the required training accuracy. Based on the type of the task to be Pf performed, the structure of the proposed neural network controller is shown in Fig.2. _ _ _ _ _-
Fig. 1. Diagram block model reference neural network efficiency optimization of a scalar control induction motor drive system
The controller will receive three input signal i.e. the speed reference signal (a *), error speed signal () *- (om) and error input power signal (Pref - Pd). The output of the controller that consist of stator voltage reference signal or modulation index (V=m,) and frequency reference signal or modulation frequency( mf) is fed to the space vector PWM modulator. In this scheme the input power reference model (Pref) block is determined as follows. With the load torque characteristic of the compressor assumed proportional to the square of the speed as given by:
b2 XI
Fig. 2.
_
Diagram block of neural network efficiency optimization controller
Tlad =k N2 L
(1)
The structure of the neural network controller consists of three layers. Based on the neuron number in each layer this structure is known as 2-3-2 network structure. The first layer is the input, which consists of two input signals XI and X2. XI received signal from the speed reference or speed command e) *, while X2 received signal from the output layer Y, as a feed back loop or recurrent structure model. By using in-start model, each of the neuron signals in the
where: Tload is compressor load torque, kL is load torque coefficient and N is motor speed=compressor speed. The power of the compressor as a mechanical motor load with friction and windage are not considered can be defined as:
Pla =load Tload N
(2)
layer is feedforward to all neurons in the hidden layer via connections between the input and the hidden ~ layers. ~input ~ ~the weight The connections weight between neuron i and] in the Ith neuron at 'tth layer respectively are represented by Wmji.
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The second layer also known as hidden layer consists of six all, a12, .. a16 respectively. Besides receiving signal from input layer, it also receives the bias signal. A transfer function of the neuron in the hidden layer at the jth neuron is defined by:
neurons
n =
n
wl,iXi + bl
a
m
=i a m J
W m. J,l
a
i
Wm J,i
Where the first term on the right hand side is defined as the Marquardt sensitivity is given by:
(6) sm =
where: nj> is neuron transfer function in hidden layer, XI is input value that has been normalized, wl,i is weight connection parameter value between input layer to hidden layer and bjl is bias parameter value in hidden layer. At the hidden layer the tangent hyperbolic activation function is employed. The neuron output function in this layer is given by:
-
(12)
i
Derivative of the neuron output function against to the weight parameter is given by:
man' = a a
(13)
j,'
l
1nl n
exp
l+ exp
Substitution result in:
(7)
-n .e
>k
~w
The output layer consist of two neurons, the first neuron is used as a reference signal frequency (Y1 =f *) and the second neuron is used as a reference signal voltage (Y2=V,*). The activation function employed in this layer is known as the linear activation function. The neuron output function in this layer is used as an output variable as given by: 2
n
2
1
Equations (12) and (13) into Equation (1) is
n j,'
sSM a' 1
(14)
The updating neural network parameters can be written by:
Xk+j
= Xk - [JT (xk )JT (xk) + dukI] JT(x, )e(xk) (15)
III.
b2
SIMULATION AND EXPERIMENTAL RESULTS
(8) r i " 'A. Simulation Results =' Simulation of the efficiency optimization of the proposed control scheme is carried out using various block developed to = 2 (9) represent the actual system using the MATLAB/SIMULINK program. The Simulink block consists of three major blocks, i.e. the three phase induction motor and compressor load block, three phase space vector PWM inverter block and the C. Real-Time Levenberg-Marquardt Learning Algorithm controller block. These blocks are designed in the S-function The important step in Levenberg-Marquardt neural network algorithm is the computation of the Jacobian matrix. For two block by employing Borland C++ program. The induction motor data are given in appendix. output neuron, the Jacobian matrix J of the neural network is given by [1 7]: To investigate the efficiency improvement of the proposed controller, two Simulink controller blocks of the proposed controller and neural network constant Volt per Hertz are developed in parallel. In order to switch the controller from the ael ael ael proposed controller to neural network constant Volt per Hertz awlll aWl ab22 or vice versa, a switch selector block is added and fed to the 1,1J1,2 (10) controller. At the start of the plot, the variable speed _ compressor motor drive system was operated by neural ae2 ae2 ae2 network constant Volt per Hertz, after the system is stable at 3 awli,i aWlii,2 ab22 second the controller is switched to the proposed controller. Back propagation derivation of Jacobian matrix weight Fig. 3 shows the response of the input power, rotor speed and parameters is described by the following function. stator voltage of the motor when the control is switched from the neural network constant Volt per Hertz to the proposed controller at speed reference command of 500 rpm. +
.
.
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Initially, the motor is run at reference speed command by using neural network constant Volt per Hertz, and maintaining the same load condition the controller is changed to the proposed
800-
controller after 3 seconds. Fig. 4 show responses of the rotor speed, electromagnetic torque, stator voltage and input power motor when the controller is switched from the neural network constant Volt per Hertz to the proposed controller at a speed reference command of 500 rpm.
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per Hertz2control are developed in the DSP controller board.
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Fig. 3. Simulation results when the controller was switched from the~~~~~~~~400 0,2
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results when the controller was switched from the i 1407
3. Simulamenta results when the controller was switched from the Ithseprmn,tveiyteefcecimrvmnofFig. tepooecotolrthsaepoeueththvbenNNV/fto the NNEOC at 1s: (a) the speed response, (b) the stator voltage thesatohaebe NN/othe propose aotroller,athe speed N/tteNOals()hsedepne()hsaovoltage
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doei h smlto nscio II. I. eeoe,response s power response.fl and (c) the input powerresponse. done In the isdvlp.rsonad()hnput spmltonsan ctsthes inseto
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The experimental results show that, by using the proposed
Florida Power and Light Company" IEEE Transaction on Energy
controller, the input power consumption and stator voltage reduce, and the speed of the motor can be maintained constant
[4] Bose, B. K.; Patel, N. R. and Rajashekara, K.. "A Neuro-Fuzzy-based
per Hertz scheme have been conducted to verify the efficiency improvement of the proposed controller. It shows
[7]
Conversion, vol. 7, No.3, pp.396-404, September1992.
in accordance to the speed reference command.
On-line Efficiency Optimization Control of a Stator Flux-Oriented Direct Vector-Controlled Induction Motor Drive". IEEE Transaction on Industrial Application. 44(2):270-273, 1997. IV. CONCLUSION [5] Zidani, F.; Said, M.S.N; Abdessemed, R.; Diallo, D. and Benbouzid, The develop t oM.E.H.. "A Fuzzy Technique for Loss Minimization in Scalar The development of the neural network control to optimize Controlled Induction Motor." Electric Power Components and Systemthe efficiency of the compressor motor at low speed operation Taylor & Francis. 30(6): 625-635,.2002. has been presented. Simulation and experiments on the variable [6] Abrahamsen, F.; Blaabjerg, F.; Pedersen, J.K.; Pedersen, J.K. and speed compressor motor drive system with neural network Thoegersen, P.B. "Efficiency-Optimized Control of Medium-Size Induction Motor Drives". IEEE Transaction on Industrial Application. efficiency optimization control and neural network constant
37(6): 1761-1767, 2001.
Volt
Kioskederis I. and Margaris N., "Loss Minimization in Scalar Control Motor Drives with Search Controllers." IEEE Transaction on
that forthe at low speed operation operation theInduction that for the proposed controller at lOW speedl the Power Electronics, vol. 11,,N.p.1 No.2, pp.213-220, Marchl1996. proposedl controller iS increased and the error efficiency significantly speed during [8] Bernal, F. F.; Cerrada, A. G. and '20 Faure, R. "Model-based Poe
Elcrois
vol
I
ac
96
andMutual 336 H inductance: 00.336 andl Mlutual inductance: HOU
loss minimization for DC and AC vector-controlled motors including core saturation". IEEE Transaction on Industrial Application. 36: 755-763, 2000. [9] Ta, C.M. and Hori, Y. "Convergence Improvement of Efficiency Optimization Control of Induction Motor Drives". IEEE Transaction on Industrial Application. 37(6): 1746-17, 2001. [10] Chakraborty, C.; Uchida, T. and Hori, Y.. "Fast Search Controllers for Efficiency Maximization of Induction Motor Drives Based on DC Link Power Measurement". Proceedings of IEEE-Power Conversion Conference. 2: 402-408, 2002. [11] Sousa G.C.D., Bose B.K. and Cleland J.G., "Fuzzy Logic Based on-Line Efficiency Optimization Control Of An Indirect Vector Controlled Induction Motor Drives" IEEE Transaction on Industrial Electronics, vol. 42, No.2, pp.192-198, April 1995.
Combined inertia: 0.00153 kg-m2
[12] Choy I.,
the efficiency optimization process can be compensated
immediately.
APPENDIX
Motor Parameters Power: 0.25hp; Voltage: 120V; Frequency: 50 Hz and Speed: 1460 rpm.
Stator resistance: 5.2 Q. and Rotor resistance: 4.0 Q.
Stator self inductance: 0.347 H; Rotor self inductance: 0.347 H
Kwon S.H., Choi J.Y., Kim J.W., and Kim K.B.," On-Line Efficiency Optimization Control of a Slip Angular Frequency Controlled Induction Motor Drive Using Neural Networks." IECON Proceedings 13 annual Conference, pp. 1216-1221, 1996 [13] Yatim A.H.M., and Utomo W.M., " On-Line Optimal Control of Variable Speed Compressor Motor Drive Using Neural Control Model." PECon Proceedings Conference, pp. 83-87, 2004 [14] Hasan K.M., Zhang L., and Singh B.," Neural Network Control of Induction Motor Drives for Energy Efficiency and High Dynamic Performance." IECON Proceedings 13 annual Conference, pp. 488-492,
ACKNOWLEDGMENT The authors would like to acknowledge the financial support for this work by Ministry of Science, Technology and Innovation (MOSTI) through Intensification of Research in Priority Areas (IRPA) program.
1997
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